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Raymond A. Opoku; Bo Pei; Wanli Xing – Journal of Learning Analytics, 2025
While high-accuracy machine learning (ML) models for predicting student learning performance have been widely explored, their deployment in real educational settings can lead to unintended harm if the predictions are biased. This study systematically examines the trade-offs between prediction accuracy and fairness in ML models trained on the…
Descriptors: Prediction, Accuracy, Electronic Learning, Artificial Intelligence
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Xintong Zhang; Jiangwei Hu; Yunqian Zhou – Education and Information Technologies, 2025
This study explores the role of perceived utility, social influence, and ethical concerns in the adoption of AI-based data analysis tools among academic researchers in China, focusing on differences between public and private universities. The research aims to identify key drivers and barriers influencing the integration of AI technology in…
Descriptors: Usability, Ethics, Artificial Intelligence, Technology Uses in Education
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Yueqiao Jin; Vanessa Echeverria; Lixiang Yan; Linxuan Zhao; Riordan Alfredo; Yi-Shan Tsai; Dragan Gasevic; Roberto Martinez-Maldonado – Journal of Learning Analytics, 2024
Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across…
Descriptors: Learning Analytics, Accountability, Ethics, Artificial Intelligence
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Gedrimiene, Egle; Celik, Ismail; Mäkitalo, Kati; Muukkonen, Hanni – Journal of Learning Analytics, 2023
Transparency and trustworthiness are among the key requirements for the ethical use of learning analytics (LA) and artificial intelligence (AI) in the context of social inclusion and equity. However, research on these issues pertaining to users is lacking, leaving it unclear as to how transparent and trustworthy current LA tools are for their…
Descriptors: Learning Analytics, Accountability, Trust (Psychology), Artificial Intelligence
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Lewis, Armanda; Stoyanovich, Julia – International Journal of Artificial Intelligence in Education, 2022
Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and…
Descriptors: Statistics Education, Ethics, Artificial Intelligence, Compliance (Legal)
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Amjad Almusaed; Asaad Almssad; Ammar K. Albaaj – International Society for Technology, Education, and Science, 2024
The impact of artificial intelligence (AI) on education and lifelong learning is a topic of significant importance as AI continues to change numerous sectors. This paper aims to critically examine AI's profound effect in these domains. The present research explores the ethical dilemmas and pedagogical approaches relevant to incorporating…
Descriptors: Ethics, Lifelong Learning, Artificial Intelligence, Computer Software